Memo: How Artificial Intelligence Can Improve Customer’s Health

Memo: How Artificial Intelligence Can Improve Customer’s Health

RE: Improving customer’s health through artificial intelligence and means to hospital success

Artificial intelligence helps in improving diagnosis in the medical and health care sectors. Some areas with its applications are robotic surgery, image analysis, among other administrative and professional health tasks. The technology is acting vital parts such as for virtual nursing assistants and in clinical judgments and is, however, not changing the patient’s role as passive treatment recipients. The technology manifests through different types by which it is helpful that are in the process of automatic business, data analysis insight, and customers’ and employees’ engagement. The prosperity of human species is dependent on survival instincts that are through proper health monitory. It is thus, essential to understanding both how this technology can improve customers’ health and means to the success of hospitals, as is the focus of this paper.

In wearable, artificial intelligence has led to the production of gadgets essential for the measurement of healthcare data. Examples of devices here are apple watch, android wear, Fitbit, and others where their users have vast data in healthcare and lifestyle (Mindsync, 2014). Essential data is available in Kardia’s system for detecting cardiovascular conditions. It also came up with a cost useful EKG wearable that took data from users for algorithm processing to record atrial fibrillation. Another system with a similar functioning is the propeller health for asthma patient attacks, which provides medication data on environmental conditions. Here, the system is vital for healthcare positive results and the processing of large data volumes such as in wearable gadgets.

There are also online platforms for fitness-related services. Some of them need the care of human respondents, such as in the personal exercise routines and weight loss diet plans. The overall result is the reduction in the size of the total amounts. It works appropriately, like in lifestyle plans, and diet and exercise routines that do not tamper with working hours and personal habits from data on own individual health. Such an idea should be on food preferences and exercise types. Eventual is the bias reduction in health conditions report and lifestyle choices with details such as individual sexual activities leading to personal wellbeing and productivity depending on the system in use because of some of the better own longevity and others in quality healthcare.

Another concept is the deep neural network for the interpretation of medical scans; endoscopy faces skin lesions, retinal images, pathology slides, electrocardiograms, among others. Receiver operating characteristics apply here too where there is the interpretation of the neural net through comparisons to a physician assessment via right positive and false positive means in a showing calculation area under the curve that is the determinacy for the accurate measurement. Websites such as the Google Assistant, Siri, and Alexa make use of personal data in the digital world beautification. The technical efficiency to these is through shopping lists, song lists, and reading lists where the users have the knowledge acquaintance on the relevant data sets.

Another dominant area where artificial intelligence applies is in radiology, with chest x-rays being the famous. The algorithm utilizing this technology from convolutional neural networks helps to identify diseases like pneumonia and cancerous pulmonary nodules. Interconnected to this is the deep neural network for learning consisting of image and speech inputs that works through multiple connected neurons for feature detection and output video providence.

Artificial intelligence surpasses its importance, such that it is implying hospital care services efficiently and precisely. Algorithms are helpful in the patient risk estimation for readmission in situations that it would not have been detectable. They follow clinical discharge criteria to do away with discharge and attune services where there are underlying resources. To patients who are very ill, they help where there is short term survival likelihood, and the possible decisions that are from them are resuscitation, using the endotracheal tube in mechanical ventilation to include invasive measures (Nature Medicine, 2019). Artificial intelligence predictive tools also guide the patients to benefit from these and those to be affected by sepsis development. Predictions too many clinical Alzheimer’s diseases to death have been through electronic record data, and the machine deep learning ones help in the clinical parameter prediction. There was a retrospective study on reinforcement on large datasets where the use of intravenous fluids, vasopressors, and other medications where there was the selection of patients’ doses. The methodology under variety was more than the humane. Cohorts size and the AUC accuracy reports were heterogeneous that needs real-world clinical setting validation.

There have been few deep learning algorithms that should help public responsibility in healthcare. An example found useful is the smartwatch algorithms that detect atrial fibrillation and another one that applies to the apple watch series. Those in the commodity are the photoplethysmography and accelerator sensors that note on the user heart rates both during rest and in physical activities (Topol, 2019). It works by giving the users haptic warnings where the ECG recording through watches and the interpretations happen through algorithms. The expected results from these are false atrial fibrillation diagnosis and possible other unnecessary evaluations in medicine. ECG draws a pattern on the smartwatch that helps in the blood potassium level detection to give essential information that would have otherwise not been obtainable.

The technology shall take part in the healthcare weaknesses in the system through the specific method of non-silicon valley hype way. In this way, it will be under the expectations of operation where it is to boost it by lowering costs. Doctors will not overwork as a result of this and reduce the possibility of committing errors in the medical profession that usually has an effect of leading to mass deaths of patients. However, there is also negativity to this where there is a rush in the implementation. Some of the ways through which it manifests are the interference of patient privacy rights, too many critics on biases and limitations, or failure in conducting services to the improvement of the people’s healthcare. Negativity to this part is the cause of disparities.

Healthcare professions can have both deep and machine learning, which is capable of glimpsing on a dataset, be in learning sessions from it, and predict outcomes from it. Data patterns that people might not capable of seeing data are not microscopic to artificial intelligence. However, the decisions might be baffling if physicians and hospitals promptly follow them. Humans have to make decisions out of these that have both financial and health consequences. An example to prove this was from the Rich Caruana, researching o Microsoft and put this forward through an engineering and technology magazine. The patients under analysis from these were low risk requiring minor intervention and not hospitalization (Hsu, 2019). These learning predictions might not also prove useful from an unusual data point encounter, for instance, a unique medical case or from the learning of peculiar and specific dataset patterns that lack a proper medical case generalization.

The best answers to the predictions are through massive data sets that lead to artificial intelligence training, especially from large populations and patient data. A sample to this was in China analysis of common childhood diseases from electronic health records. Extensive data brings problems also when their algorithm is in a new population. Medical centers tend to attract a specific type of patient. The artificial intelligence models need to be under real patient experimental clinical research. In the medical profession, there will be frequent auditing for fairness and accuracy from various ethnic, gender, ages, and health insurance groups. It is the way artificial intelligence is incorporating other fields to involve biases. The mistakes from them would not be void if there will be a legal liability (Hsu, 2019). Medical devices that use one regulatory approval will be under additional reviews each time there is new data.

There is mind unification with the machine for brain-computer interfaces. Patients can use computers for communication and direct interface creation for the technology and mind where keyboards, mice, and monitors with their implications apply. Another concept is the brain-computer interfaces that solve problems such as neurological diseases and nervous system trauma. The difficulties of speaking inability, movement, and meaningful interaction will be under resolution. Here is the restoration of the neural activities and their actions through some technology like the tablet computer and phone. Other patients inclusive in this category are strokes, locked-in syndrome, and those with spinal cord injuries.

The development of radiology tools for the next generation is also imminent. Machines take radiological images such as the CT scanners and x-rays that show the internal structure of the body and their functions. Other diagnostic processes are through physical tissue samples from biopsies that can potentially lead to infection. The prediction that is from experts shows that the radiology tools will be perfect and accurate; thus, they are likely to take the part of the tissue samples. The goal is to make it one between all the teams involving diagnostic imaging, surgeons, international radiologists, and pathologists (Bresnick, 2018). The methodology is vital in the understanding of the work of tumors from a full potential than a small basement to a small segment malignancy. It also follows the understanding of cancer aggressiveness by the target treatments.

The care and access in underserved and developing regions are under expansion. It mostly refers to the world developing nations where there are trained healthcare shortages such as ultrasound technicians and radiologists. There are many of these experts working in half a dozen hospitals. Artificial intelligence here is mitigating the severe deficit impacts in the clinical staff through performing some diagnostic duties that should be for humans. Where there are screening and imaging tools, do not necessarily need to be the presence of radiologists. ”The potential for this tech to increase access to healthcare is tremendous, ‘said Jayashree Kalpathy-Cramer, Ph.D., Assistant in Neuroscience at MGH and associate professor of radiology at HMS. Disparate ethnic groups and different regions have different physiological and environmental factors for the disease presentation influence. The causal agents to disease and the populations under the effects are different in each area. The data to choose should, therefore, be for disease diversity presentations and people.

Electronic burden use is as well under reduction in records. It involves the digitalization journey for instrumental roles in the healthcare industry, although there are myriad potential problems with associations in cognitive overloads, user burnout, and endless documentation. Developers are making use of this technology in the intuitive interface creation and automation of routine processes to reduce users’ time consumption. The time under reference here is in three tasks that are order entry, clinical documentation, and the in basket sorting out (Bresnick, 2018). The clinical documentation problem is under improvement through voice recognition and dictation with others incorporating the use of natural language processing tools. It also helps in the routine inbox request processing, such as in result notifications and medication refills. Other tasks in prioritization with attention need from clinicians and landsman enables users to work harmoniously via their jobs.

An aspect is essential in containing the antibiotics risk resistance, which is an emanating population threat. Drugs overuse has an effect of causing superbug evolution that has no treatment response. Organisms that are multi-drug resistant may cause a disaster in a hospital that may cause massive death. Data on electronic health affect infection pattern identification through which many patients can know their disease status before the symptoms. The health care providers will have alerts on the leveraging machine learning from analytics to create their learning and cause accuracy and increase speed. The tools used can even help in the control of infections and the resistance to antibiotics.

In pathology, there is more precise analytics for images which have relevant diagnostic data in the care spectrum delivery. The majority of decisions in health care are a result of pathology results. There is pixel-level analytics on digital images that have the aspects of human eye escaping nuances. It is going to the level of job assessment for cancer progress from the basis of clinical staging and histopathology grade that will detest huge advancements. Before any human clinical data review, feature identification in slides is through artificial intelligence. The critical part of the slides is known by artificial intelligence through slides where there is the proper direction to the right things. Pathology use efficiency increases from the time assessment on each case.

Intelligence levels in the medical devices and machines are under improvement brought by artificial intelligence. The factual points here are the incorporation of the smart device in the consumer environment through real-time videos in refrigerators with other situations of cars having detections to any driver distractions. These intelligent devices are for patient monitoring. Deterioration is also known in artificial intelligence ability reducing the possibility of hospital penalties. Patients get the supposed care through timely adherence and also decreasing the burdens to physicians.

Decision making in the clinical sector is reducing from the revolutionization and decision making possible through artificial intelligence at the bedside (Bresnick, 2018). There is an imminent shift from free for service and reactive care. Chronic disease, costly acute events, and deterioration are what all individuals aim for, where reimbursement structures help in the process development to that which is proactive and predictive. The part artificial intelligence is playing here is in the predictive analysis and support tools for clinical decisions for early problem identification. Earlier warnings to diseases and other conditions apply, such as seizures with high intensive data set requirements.

For the part of ensuring a successful hospital is first by having the necessary tools such as guide books. Visual management is as well famous for waste reduction. There should be proper rational clarification to cover what organizations ought to accomplish as a group or individually (VMG, 2017). Value creation is also essential to have adequate value care. There should be proper and initial communication to extend over the connection to have critical constituents. The final aspect of these is culture blending. It applies most where there are mergers or affiliations because the beliefs and norms affect the individual’s behaviors in the hospital fraternity.

To sum up, hospitals do essentials organizations in society owe to the roles they undertake. Getting acquaintance on their rationale is vital, especially ad to what the new technology pertains to. Technology improvements are impacting positively, and thus the incorporation of artificial intelligence in the health sector is essential. Human health conditions vary virtually every day from the aspects of the revolutions. As technology improves, then the new ideas come up through artificial intelligence to curb the effects of many diseases like the ones under discussion. In turn, it is a definite improvement to the health sector that comes from the technology and therefore leading to success. The use of technology in health sectors should consequently continue, and specialists are to embrace new improvements in this sector.

References

Davenport Thomas H and Ronanki Rajeev (2018). Article technology. Artificial intelligence for the real world. Don’t start with moon shots. Harvard business review.

Graban, M. (2016).Lean Hospitals: Improving quality, patient safety, and employee engagement,3^rd edition.Excerpt from chapter 6.(5S and visual management).

Hsu, Jeremy (2019).Will artificial intelligence improves health care for everyone? Smithsonian magazine. Updated 31 July 2019 at http://www.smithsonianmag.com/inovation/will-artificial-intelligence-improve-healthcare-for -everyone-180972758/

Mindsyc (2019).How artificial intelligence can improve health and productivity. Retrieved at http;//medium.com/mindsync-ai/how-artificial-intelligence-can-improve-health-and-productivity-1a9e666a9d6c.

Topol Eric J. (2019).High-performance medicine: the convergence of human and artificial intelligence. Nature medicine review article/focus. 25. 44-56.

VMG Health (2017). 4 Ways to ensure a successful hospital merger or acquisition. Becker’s Hospital Review. Hospital transactions and valuation.www.beckershospitalreview.com.